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Measurements results assessment and emission limits
Čech, Martin ; Jedlička, Filip (referee) ; Dvořák, Radek (advisor)
Main content of this task is summary and explanation of main methods,which can be used to evaluation the results from measuring of pollutants in combustion gases. The way which should be most acceptable for our datas will be consequently used in a simply example. There will be mentioned the importace of chosing the entering datas and their dependence on outer conditions. Another chapter will be devoted to emission standarts of main pollutants in Czech republic and in chosen world countries too. There will be mentioned the historical developement of these standarts and hypothesis to the future about this question too.
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Adaptive Control of a DC Drive
Krkoška, Roman ; Huták, Petr (referee) ; Skalický, Jiří (advisor)
The master’s thesis deals with method of adaptive control of electrical drives. It aims at control of DC motor with using self tunning controller (STC) and gain-schedulink in simulative enviroment of MATLAB. The first part of the thesis deals with of basic methods adaptive control of electrical drives and with of basic identification methods, computation of controller parameters and control laws of controllers. The second part of the thesis is aimed at applying adaptive controllers to mathematical model of controlled system. Include monitoring parameters of identification The thesis contains originate programs include source files.
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Mathematic model of steam turbine
Kroliczek, Filip ; Dokoupil, Jakub (referee) ; Pivoňka, Petr (advisor)
The goal of this thesis was to create a mathematical model of a steam turbine based on the data acquired by measurement, and to verify its behaviour. The first part contains research, which is supposed to introduce basic principles of the steam turbine and description of important construction parts and the possibilities of control. The second part describes the experimental identification method of least squares, used for the calculation of an ARX model of the steam turbine. Finally, the last part focuses on the program environment used for creation of the mathematical model and explanation of measured data analysis process. Furthermore this segment describes the created simulation program as well as a visualisation of the dynamic processes in the steam turbine, including the design of control.
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Processing of Correlated Measurements
Gerhátová, Ľubomíra
Correlation is a standardized rate of stochastic dependence between a pair of random variables. The empirical correlation coefficient characterizes the degree of dependence between realizations of a random variable, e.g., between individual independent measurements, between measurements of different types, between measurements with different devices or observers, between measurements with different technologies or procedures, between measurement results and the external environment, etc. The ideal situation is when the measurements are stochastically independent of each other, and thus the correlation coefficient between individual by measurements is equal to zero (corresponds to the diagonal covariance matrix of the measurement). This state is ensured in practice by alternating between different measurements, by alternating observers, instruments, measurement methods, etc. Using simple examples, we will show how the processing results will change with the use or non-use of correlations between measurements.
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Local polynomial regression
Cigán, Martin ; Bašta, Milan (advisor) ; Maciak, Matúš (referee)
This thesis examines local polynomial regression. Local polynomial regression is one of non-parametric approach of data fitting. This particular method is based on repetition of fitting data using weighted least squares estimate of the parameters of the polynomial model. The aim of this thesis is therefore revision of some properties of the weighted least squares estimate used in linear regression model and introduction of the non-robust method of local polynomial regression. Some statistical properties of the local polynomial regression estimate are derived. Conditional bias and conditional variance of the local polynomial regression estimate are then approximated using Monte Carlo method and compared with theoretical results. Powered by TCPDF (www.tcpdf.org)
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Local polynomial regression
Cigán, Martin ; Bašta, Milan (advisor) ; Maciak, Matúš (referee)
This thesis examines local polynomial regression. Local polynomial regression is one of non-parametric approach of data fitting. This particular method is based on repetition of fitting data using weighted least squares estimate of the parameters of the polynomial model. The aim of this thesis is therefore revision of some properties of the weighted least squares estimate used in linear regression model and introduction of the non-robust method of local polynomial regression. Some statistical properties of the local polynomial regression estimate are derived. Conditional bias and conditional variance of the local polynomial regression estimate are then approximated using Monte Carlo method and compared with theoretical results. Powered by TCPDF (www.tcpdf.org)
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